Policeman kills colleague while dispersing crowd in Lagos
by Sanya AgunbiadeA policeman reportedly killed his colleague while dispersing a crowd in the Igando area of Lagos.
The officer attached to the Igando Police Division reportedly shot the other officer dead along LASU-Igando road.
In a report by The Punch, the killer cop shot into the air to disperse the crowd, killing his colleague and injuring a ride-hailing motorcyclist in the process.
It was gathered that the police officer and the deceased were members of the police team that was mobilised to a section of the road on Saturday, November 23, 2019, to arrest motorcyclists plying prohibited routes and riding against traffic.
Trouble started when the team arrested a commercial motorcyclist for allegedly breaking traffic rule, but the rider resisted arrest and struggled with the policemen who wanted to seize his bike.
A witness, Kunle Gbadamosi, revealed that the Police killed his colleague after he cocked his gun to shoot into the air so as to disperse the crowd.
He said:
“I was on a bus en route to Igando from Iyana-Iba when I chanced on the incident. It caused traffic and I decided to alight from the bus. I saw a policeman lying lifeless on the floor. There was confusion everywhere.
“I learnt that some policemen were trying to arrest motorcyclists riding against the traffic and on prohibited routes. One of the motorcyclists they were trying to arrest resisted and his co-riders rallied round him because they felt the policemen wanted to extort him.
“One mobile policeman, who was trying to assist the policemen to get out of the situation, cocked and fired into the air to disperse the crowd. He shot and killed a policeman in the process.”
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mne.viz.plot_compare_evokeds¶
-
mne.viz.
plot_compare_evokeds
(evokeds, picks=None, gfp=False, colors=None, linestyles=['-'], styles=None, cmap=None, vlines='auto', ci=0.95, truncate_yaxis='max_ticks', truncate_xaxis=True, ylim=None, invert_y=False, show_sensors=None, show_legend=True, split_legend=False, axes=None, title=None, show=True)[source]¶ Plot evoked time courses for one or more conditions and/or channels.
- Parameters
- evokedsinstance of
mne.Evoked
|list
|dict
If a single Evoked instance, it is plotted as a time series. If a dict whose values are Evoked objects, the contents are plotted as single time series each and the keys are used as condition labels. If a list of Evokeds, the contents are plotted with indices as labels. If a [dict/list] of lists, the unweighted mean is plotted as a time series and the parametric confidence interval is plotted as a shaded area. All instances must have the same shape - channel numbers, time points etc. If dict, keys must be of type str.
- picks
str
|list
|slice
|None
Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g.,
['meg', 'eeg']
) will pick channels of those types, channel name strings (e.g.,['MEG0111', 'MEG2623']
will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick all data channels.If picks is None or a (collection of) data channel types, the global field power will be plotted for all data channels. Otherwise, picks will be averaged.
If multiple channel types are selected, one figure will be returned for each channel type.
If the selected channels are gradiometers, the signal from corresponding (gradiometer) pairs will be combined.
- gfpbool
If True, the channel type wise GFP is plotted. If None and picks is None or a (list of) channel type(s), this is set to True.
- colors
list
|dict
|None
If a list, will be sequentially used for line colors. If a dict, can map evoked keys or ‘/’-separated (HED) tags to conditions. For example, if evokeds is a dict with the keys “Aud/L”, “Aud/R”, “Vis/L”, “Vis/R”, colors can be dict(Aud=’r’, Vis=’b’) to map both Aud/L and Aud/R to the color red and both Visual conditions to blue. If None (default), a sequence of desaturated colors is used. If cmap is None, colors will indicate how each condition is colored with reference to its position on the colormap - see cmap below. In that case, the values of colors must be either integers, in which case they will be mapped to colors in rank order; or floats between 0 and 1, in which case they will be mapped to percentiles of the colormap.
- linestyles
list
|dict
If a list, will be sequentially and repeatedly used for evoked plot linestyles. If a dict, can map the
evokeds
keys or ‘/’-separated (HED) tags to conditions. For example, if evokeds is a dict with the keys “Aud/L”, “Aud/R”, “Vis/L”, “Vis/R”, linestyles can be dict(L=’–’, R=’-‘) to map both Aud/L and Vis/L to dashed lines and both Right-side conditions to straight lines.- styles
dict
|None
If a dict, keys must map to evoked keys or conditions, and values must be a dict of legal inputs to
matplotlib.pyplot.plot
. These parameters will be passed to the line plot call of the corresponding condition, overriding defaults. E.g., if evokeds is a dict with the keys “Aud/L”, “Aud/R”, “Vis/L”, “Vis/R”, styles can be {“Aud/L”: {“linewidth”: 1}} to set the linewidth for “Aud/L” to 1. Note that HED (‘/’-separated) tags are not supported.- cmap
None
|str
|tuple
If not None, plot evoked activity with colors from a color gradient (indicated by a str referencing a matplotlib colormap - e.g., “viridis” or “Reds”). If
evokeds
is a list andcolors
is None, the color will depend on the list position. Ifcolors
is a list, it must contain integers where the list positions correspond toevokeds
, and the value corresponds to the position on the colorbar. Ifevokeds
is a dict,colors
should be a dict mapping from (potentially HED-style) condition tags to numbers corresponding to positions on the colorbar - rank order for integers, or floats for percentiles. E.g.,evokeds={"cond1/A": ev1, "cond2/A": ev2, "cond3/A": ev3, "B": ev4}, cmap='viridis', colors=dict(cond1=1 cond2=2, cond3=3), linestyles={"A": "-", "B": ":"}
If
cmap
is a tuple of length 2, the first item must be a string which will become the colorbar label, and the second one must indicate a colormap, e.g.cmap=('conds', 'viridis'), colors=dict(cond1=1 cond2=2, cond3=3),
- vlines“auto” |
list
offloat
A list in seconds at which to plot dashed vertical lines. If “auto” and the supplied data includes 0, it is set to [0.] and a vertical bar is plotted at time 0. If an empty list is passed, no vertical lines are plotted.
- ci
float
|callable()
|None
| bool If not None and
evokeds
is a [list/dict] of lists, a shaded confidence interval is drawn around the individual time series. If float, a percentile bootstrap method is used to estimate the confidence interval and this value determines the CI width. E.g., if this value is .95 (the default), the 95% confidence interval is drawn. If a callable, it must take as its single argument an array (observations x times) and return the upper and lower confidence bands. If None or False, no confidence band is plotted. If True, a 95% bootstrapped confidence interval is drawn.- truncate_yaxisbool |
str
If not False, the left y axis spine is truncated to reduce visual clutter. If ‘max_ticks’, the spine is truncated at the minimum and maximum ticks. Else, it is truncated to half the max absolute value, rounded to .25. Defaults to “max_ticks”.
- truncate_xaxisbool
If True, the x axis is truncated to span from the first to the last. xtick. Defaults to True.
- ylim
dict
|None
ylim for plots (after scaling has been applied). e.g. ylim = dict(eeg=[-20, 20]) Valid keys are eeg, mag, grad, misc. If
None
, the ylim parameter for each channel equals the pyplot default. Defaults toNone
.- invert_ybool
If True, negative values are plotted up (as is sometimes done for ERPs out of tradition). Defaults to False.
- show_sensors: bool | int | str | None
If not False, channel locations are plotted on a small head circle. If int or str, the position of the axes (forwarded to
mpl_toolkits.axes_grid1.inset_locator.inset_axes
). If None, defaults to True ifgfp
is False, else to False.- show_legendbool |
str
|int
If not False, show a legend. If int or str, it is the position of the legend axes (forwarded to
mpl_toolkits.axes_grid1.inset_locator.inset_axes
).- split_legendbool
If True, the legend shows color and linestyle separately; colors must not be None. Defaults to True if
cmap
is not None, else defaults to False.- axes
None
|matplotlib.axes.Axes
instance |list
of axes | “topo” What axes to plot to. If None, a new axes is created. If “topo”, separately for each channel type, a new figure is created with one axis for each channel in a topographical layout. In this case, gfp is ignored. When plotting multiple channel types, can also be a list of axes, one per channel type.
- title
None
|str
If str, will be plotted as figure title. If None, the channel names will be shown.
- showbool
If True, show the figure.
- evokedsinstance of
- Returns
Notes
When multiple channels are passed, this function combines them all, to get one time course for each condition. If gfp is True it combines channels using global field power (GFP) computation, else it is taking a plain mean.
This function is useful for comparing multiple ER[P/F]s - e.g., for multiple conditions - at a specific location.
It can plot:
a simple
mne.Evoked
object,a list or dict of
mne.Evoked
objects (e.g., for multiple conditions),a list or dict of lists of
mne.Evoked
(e.g., for multiple subjects in multiple conditions).
In the last case, it can show a confidence interval (across e.g. subjects) using parametric or bootstrap estimation.
When
picks
includes more than one planar gradiometer, the planar gradiometers are combined with RMSE. For example data from a VectorView system with 204 gradiometers will be transformed to 102 channels.


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Housekeeping
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Hard to enforce smoking ban in pasar malam, says deputy health minister
IPOH, Nov 1 — The Health Ministry is willing to study a suggestion to demarcate pasar malam (night market) as a smoking-free zone but it could be tricky to enforce, said Deputy Health Minister Dr Lee Boon Chye.
“This is due to the area not having legal boundaries like a food premise, public building or higher learning institution which has clear boundaries.
“As an example, if we try to enforce the law at a pasar malam, they can dispute that it isn’t the pasar malam, it’s their front yard, so there is difficulty in carrying it out,” he told reporters after officiating the 31st Annual Scientific Congress of Malaysian Oncological Society 2019 today.
There has been a smoking ban in place at food premises nationwide since January 1 this year, with strict enforcement to begin from January 1 next year, therefore anyone caught contravening Regulation 11 of the Control of Tobacco Products Regulations 2004 can be fined up to RM10,000 or imprisoned a maximum of two years.
On a different topic, Dr Lee said Malaysia requires more oncologists to provide sufficient care to cancer patients as at the moment there are 42 specialists in public and university hospitals, and 73 serving in private hospitals.
“We need more than 200 oncology specialists so we are definitely encouraging more doctors to specialise by joining a master’s programme at a local university or taking it up as a subspecialisation,” he said. — Bernama


Shaffer Lab, UCLA
I earned by PhD in Biology from UCLA in June 2019. I conducted my work in the Shaffer Lab in the Department of Ecology and Evolutionary Biology and my dissertation is titled, "An Integrative Population and Landscape Genomic Approach to Conservation of a Threatened California Amphibian at Multiple Spatial Scales." I am interested in the open access of science and knowledge; blockchain and token economies and their potential role in streamlining science and data validation; and in the application of genomics and spatial analysis to biological conservation.


The following lines should all say "PASS":
PASS
PASS
ΡΑSS
The following character should be a smiley face:
☺


Biography
I was born in germany on a army base. We bounced around a lot in my early years. Memories from that age are fuzzy. You know the feeling that you are never sure if you are recalling first hand accounts or an impartal memory from a photo you once saw.
After bouncing around for years I ended up in red bluff. I then moved to chico ca for all the wrong reasons. This is were I have set up roots.
I enjoy all the normal things. Movies, tv, games and cooking.
My educational experiences have ranged from bad to worse. I have gone to college a few times trying to get my degree done. I just never seam to have enough money to get it done. I for the most part am self tought IT and coding. The school of hard knocks is a lot harder then you would think.
Something quirky about me is that I can pick up things with my feet. I have a bad back so I use my feet to pick things up off the floor and hand... no foot them to my hands.
Thank you for reading about me.
Written by
Alex Williams


--- layout: post title: Quick PDF generation with RTex categories: - sightings - releases - tools author: marcel published: true date: 2006-03-19 23:02:00.000000000 +00:00 ---
Bruce Williams of Naviance recently announced his RTex plugin. It exposes your controller data to rtex
views that output LaTeX which is convereted to PDF.
To install you can use the plugin script:
ruby script/plugin install rtex
Or grab it from svn:
svn co http://codefluency.com/svn/codefluency/rails/plugins/rtex
People have used PDF::Writer to generated .rpdf views. Why go through LaTex to get to PDF rather than use PDF::Writer? Speed, says Bruce.


Born and raised in San Francisco, CA. I live in Upland, CA currently. I was a professional international DJ for 16 years and a martial artist for 20 years. I love everything science and arts. Add me on Facebook!
Lets learn more about this swell guy!Anything mexican and some healthy food here and there.
I love podcasts of all types. Workaholics to relax, Planet Earth to study.
Anything that challenges my brain cells. I love anything outdoors and nature related. Gardening and martial arts are my places of zen.
Life is hard enough, dont make it harder for anyone else.


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Neural Network Basics¶
Classification problem¶
There are various applications of neural-network models. In this section, for simplicity of discussion, we will focus only on the classification problem. In the classification problem, when the number of output classes is \(V\), then the class index \(v\) may take a value in the following range:
In stead of the above sparse representation of the output class, we may use the one-hot vector representation as below:
Sometimes, the class index \(v\) in (1) is referred to as sparse representation compared to the one-hot vector representation in Table
Model¶
In the classification problem, the model can be considered a function to predict the output class probability \(\bsf{y}\) given the input \(\bsf{x}\). Thus when the model is represented by a function \(f\), the relation is represented by the following equation.
Training Data Set¶
In this section, we consider the supervised training case, which might be the most basic case of training neural-network models. Other cases such as unsupervised or semi-supervised training cases will be covered later.
where \(N_{\text{tr}}\) is the number of examples in the training set \(\mathcal{T}\).
Loss Function¶
In training neural network models, our objective is making the model output \(\hat{\bsf{y}}\) as close to the ground truth \(\bsf{y}\) as possible. To perform this task analytically, we usually define a certain loss function and minimize it.
The entire loss \(\mathbb{L}\) is defined by the following equation for the entire training set defined in (3):
where \(N_{tr}\) is the number of training examples as defined in
Now, the question is what would be a good candidate for the loss function in (4). Various types of functions may be condidates for this loss function including the well know \(L_1\) or \(L_2\) losses and so on. As will be discussed later, an appropriate loss function is different depending on applications.
In the classification problem, we would like to make the distribution of prediction by the model \(\hat{\bsf{y}} = f(\bsf{x})\) as close to the distribution of the training data set as possible. In this classification problem, note that both the ground truth output \(\bsf{y}\) and the predicted output \(\hat{\bsf{y}}\) are probability distributions of the set of output labels defined in (1).
To measure the difference between two distributions, we may use the KL divergence or the Cross Entropy (CE). Since the distribution of the training set is already fixed, minimizing the following Cross Entropy (CE) loss is equvalent to minizing the KL divergence, which is shown in the following equation:
From the definition of cross entropy (CE), \(\mathbb{L}_{\text{CE}}\) is given by the following equation:
When the one-hot vector representation is used in (5), (5) is calculated as follows:
where \(y_v\) and \(\hat{y}_v\) are \(v\)-th element of the one-hot vector \(\mathbf{y}\) and \(\hat{\mathbf{y}}\) respectively, and \(V\) is the number of output classes as shown in (1).
(6) may be expressed in summation rather than the expection as follows:
where \(y^{(k)}_v\) and \(\hat{y}^{(k)}_v\) represent the \(v\)-th element of the one-hot vector of the k-th training example \(\mathbf{y}^{(k)}\) and the corresponding predicted output using the model \(\hat{\mathbf{y}}^{(k)}\).
In Tensorflow, cross entropy is calculated using tf.nn.softmax_cross_entropy_with_logits for one hot vector representation and tf.nn.sparse_softmax_cross_entropy_with_logits for sparse matrix representation.
Gradient Descent¶
Suppose that the entire parameters of our neural network model is denoted by \(\mathsf{w}\). If we can directly differentiate the loss function \(\mathbb{L}\), we improve the model parameters using the well-known Gradient Descent approach as shown below:
where \(\mu\) is a constant called the learning rate.
It is well known that the direction of the negative of gradient is the direction of steepest descent in the surface defined by the loss function \(\mathbb{L}\). However, Gradient Descent (GD) is not a practical approach when the training set size is sufficiently large for the following two reasons.
Inefficiency in computation
It is usually not possible to load all the training examples in memory at once. Thus, we may need to store partial results and part of inputs in the disk, and repeatedly load and save partial result.Slow convergence
Parameter update is done only once after processing the entire training set. That is to say, the parameter update is done only once for one epoch.
Stochastic Gradient Descent (SGD)¶
Unlike the GD approach using the loss from the entire training dataset, in SGD, we update the parameter for each training example \((\bsf{x}^{(k)}, \bsf{y}^{(k)})\) iteratively.
where \(\mathbb{L}^{(k)}\) is the loss value calculated using only a single training example \((\bsf{x}^{(k)}, \bsf{y}^{(k)})\) and the prediction by the model \(\hat{\bsf{y}}^{(k)}\):
Back-Propagation¶
Questions¶
Q1. Calculate the cross entropy between the following two vectors containing probability distributions:
Q2.


Death toll rises to 65 in Kenya flooding
Parts of the country could continue to face destructive flooding this week.
Heavy rains in Kenya unleashed a deluge that killed five people overnight in the Rift Valley, raising the death toll from weekend flooding to 65.
The five were returning home after attending a wedding in Tanzania when their car was washed away, said Daudi Loronyokwe, a police chief in Kajiado Central community.
The victims included three children, he said.
Governor John Lonyang’apuo said between 80,000 and 120,000 people have been affected in West Pokot county, the worst hit by the floods and mudslides.
The governor said at least 52 people were confirmed dead there.
He described how his wife managed to struggle to safety, clutching another child, after the mudslide covered their home while they slept.
The torrential rain is uncommon for this time of year. Parts of the country could continue to face destructive flooding this week, the Kenya Red Cross said.
“No one can sleep in their homes for now because we don’t know when the next landslide will occur,” resident Peter Lokor said.
“So many people have been affected, actually all those who live in this area, and everybody is afraid of going back home.”
The Kenya Red Cross cited what it called a record-breaking temperature rise in the Indian Ocean in recent weeks that also has caused flooding in South Sudan and Somalia.
Now Djibouti is affected. The Horn of Africa nation’s capital reportedly saw more than three times its annual rainfall between Thursday and Sunday alone, the United Nations humanitarian office said.


Ship.js
An arcade style top scrolling game in HTML5. A work in progress..
- Use the Arrow Keys to move (You can also use left click)
- Press Control to shoot.
- Press P to pause (and again to resume)
- Press S to toggle sound (experimental: you may need a beta or nightly build of your browser for good results)
- Press R to toggle free range mode.
Ship.js is on github: https://github.com/tomwadley/ship.js
The version playable here is probably the latest commit on the master branch
Contact me: tomwadley [at] fastmail [dot] fm
Ship.js is Copyright (C) 2012 Tom Wadley and released under the MIT licence


--- layout: default title: srchulo.com | Open Source Projects by Adam Hopkins ---
I'm Adam Hopkins, a computer science major at the University of Texas Austin. I like perl, the beach, and studying languages. I'm also fond of my guitar.


--------------------------------------------------------------------------------------------------------------------------------------
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3
Human Resources Policies
company.
In case of miscarriage, the female employee shall, on production of a
medical certificate stating this, will be entitled to leave with salary at
the rate of maternity benefit, for a period of six weeks immediately
following the day of her miscarriage.
A female employee suffering from illness arising out of pregnancy,
delivery, premature birth of child or miscarriage shall, on production
of a medical certificate, be entitled, in addition to the above
mentioned period of absence allowed to her, as the case may be, to
leave with salary at the rate of maternity benefit for a maximum
period of one month.
Note:
Paternity leave for male employees : All male employees who have
been associated with the company for a period exceeding 6 months shall
be eligible to claim a leave of 15 paid days within 1 month of the birth of
their child for a maximum of two times during the tenure of their service
in the company.
General
Guidelines
1.
Intervening declared holidays/weekends falling in between the leave
period availed will not be counted for the purpose of calculation of
leave (except maternity leave) availed by the employee.
2.
Leaves can be clubbed together by an Employee provided the criteria
for availing any particular leave are duly met.
3.
No leave as mentioned above can be claimed as a right by the
employee. Though the management encourages employees to avail
leave permissible to any employee, it shall be the discretion of the
management to disallow any application for leave if the business so
demands or in case of any exigency of work.
4.
Entitlement and provision of leave as mentioned above are subject to
modification at any time during the year, should the management
decide so. Any change in the provisions of leave shall be informed to
employees and that shall be binding on all the employees.
5.
Any wilful absence from the work will attract disciplinary action.
6.
Where interpretation/ clarification of any of these leave rules
mentioned above is required, it shall be referred to the Admin Head
and his/ her decision shall be final and binding.
7.
If the employee leaves the organization during the year and has
already consumed more CL/SL than the pro-rata leaves accrued, then
the same will be adjusted in his F&F settlement.
Contact
For any clarification kindly contact the GA and HR Department.
Approval
Approved by:
Date: 1
st
Dec 2015
Director


Do you see the "Feedback" button to the right? This is how the STOMT website widget looks like. Always just one click away users can leave valuable feedback.
To install the stomt JavaScript-SDK you only have to add the following lines to the bottom of you html file and adjust the "appId" at the bottom of the script to your business profiles username:
Add a STOMT creation form on your website to engage with our community or customers. Integrate it after a checkout, logout or other customer interaction to get their feedback as fresh as possible. To add the form have a look at our full documentation.
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To integrate it copy the widget code from above and add the "addFeed" command as documented here.


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--- layout: archive title: Zulip Chat Archive permalink: /stream/113489-new-members/topic/Hi!.html ---
Hi all!
I'm a graduate student at MIT. I'm new to Lean, but I've been interested in learn about Lean (and might actually get onto it now that it's summer). Anyway, I have a few talented high school students (i.e. comfortable with a good chunk of undergraduate math) and they've been looking for stuff to do over the summer. It seems to me that this might be an appropriate place for them to find something to contribute to - but I'm a bit unfamiliar as to what's already done / being worked on etc. I would really appreciate it if someone could give me some pointers.
Thanks!
Welcome!
Most of us mathematicians are struggling quite hard to formalise basic things (-;
I'm not exactly sure what kind of contributions you are thinking of... mathematical things or more recreational things?
To give you an idea: at the moment it is a gigantic task to show that the derivative of exp
is exp
. And indeed, this task has not been done yet.
On the other hand, we have some pretty advance commutative algebra done
So the library is very skewed.
To be honest I'm not sure what kind of contributions I'm thinking of either. Is there a place where I can see something like 'ongoing tasks' / 'what we have so far' etc.?
https://github.com/leanprover-community/mathlib/pulls?page=1&q=is%3Apr+is%3Aopen
https://github.com/leanprover-community/mathlib/commits/master
This seems like what I've been looking for. Thanks!
Are you in the maths department there?
@Kenny Lau will be at MIT next semester; he's a big contributor to mathlib.
Yeah! I've talked to Kenny - in fact he recommended me to show up in this chat
To give you an idea: at the moment it is a gigantic task to show that the derivative of
exp
isexp
. And indeed, this task has not been done yet.
Is it really a gigantic task? This is just a question of showing a power series can be differentiated term by term within its radius of convergence. I guess we're still waiting for the differentiation API but gigantic? Isn't it just a bit tedious and long?
Very recently there's been a bit of interest here in the 100 theorems list. A lot of easy items on the list haven't been done, and there's quite a range of potential projects.
For example proving that the harmonic series diverges might be a good first project.
@Chun Hong Lo that would make a very interesting project. For many of the theorems on the list, people here would know in theory how to do it, it's just finding the time.
Many people here have got their own projects that they're working on.
(Also see https://github.com/leanprover-community/mathlib/blob/100-thms/docs/100-theorems.md, http://bim.shef.ac.uk/lean/hundred_theorems.html.)
One issue is that many of these are kind of useless from a math library perspective, but for a first project that's not necessarily such a bad thing
In fact I know from experience that many first projects by beginners are in no shape at all to go into a maths library.
So we can just point to it and say someone did it, and that's fine
And the student gets to make the number go up
The theorems are mostly useless but the library building needed to prove the theorems are not
Thanks for all the references -- it makes sense that first projects by beginners are unlikely to be appropriate for the maths library but that's absolutely fine. I'll probably be back with more concrete questions later, once I learn some of the basics first :)
There's also the issues list, https://github.com/leanprover-community/mathlib/issues, which I'd love to see become a clearing house for good problems for newcomers to work on.
I have some random mathematical issues which I still didn't add to the issues page, but the problem is that all of them will involve creating some new structures, and there is an art to this which a beginner probably will not have.
Ok, maybe gigantic was a bit of an exaggeration. But it is definitely a lot more work than a mathematician would naively expect it to be.
Hi all, I'm new too, and my uninformed opinion is that analysis is much harder to formalize than algebra, topology, category theory... because of the many different definitions one could make about such simple objects as exp
. As someone remarked on MathOverflow, exp
has three natural definitions: power series, inverse to log=\int dx/x, and lim(1+x/n)^n. If we want to prove a specific fact about exp
, we could choose the easiest definition for that purpose; but for a formalization project, we'd need to have all three, and prove their equivalence. It's further complicated by the fact exp
is also a complex-analytic object.
Same goes with (constructions of) real numbers.
I know of 3 different definitions of the p-adic integers...
The good thing is that all the different definitions of exp are actually equal (and not just isomorphic)
I think one thing that is perhaps holding up analysis is that maybe we don't know (at least I certainly don't know) what the optimum level of generality is. For example it wouldn't be hard to prove the theorems from a real analysis course on convergent series, but it's clearly not the right level of generality.
But I never learned enough analysis to know what the correct level of generality is
Johannes made multivariate polynomials but it became clear quickly that we still needed single-variable ones so Chris made them too.
I think I did them wrong though. All the structure should have come from mv_polynomial
first to shorten the theory
Exactly. An obvious first goal would be the fundamental theorem of calculus, and there already are multiple issues with the level of generality, the least of which being: Are functions real-valued or complex-valued (if you have Cauchy's integral formula in mind), or as Bourbaki would have it, taking values in a topological vector space? I always felt this was excessive, but when I did sit down and try to write it down, that's what I ended up too...
Do remember that Bourbaki (or Weil in particular) started out trying to write a text for analysis, which finally came after volumes of algebra and topology—though it was only "functions of a single real variable" and doesn't seem to have much on convergence of series, if I recall. It's probably at the "optimum level of generality" as it gets, being Bourbaki after all.
Not totally unrelated, there is a rumor that Bourbaki was working on a volume on differentiable manifolds, and Grothendieck wanted to base it on sheaves and categories... they ended up with only a "summary of results" on manifolds. We all wonder what it would have been like if Grothendieck had his way.
I think @Patrick Massot has a pretty good idea
Analytic functions in several variables are done in proper generality for instance in Mujica, complex analysis in Banach spaces (see the first chapter there). But this will be so much more complicated than one single variable that I think it is definitely worth it to do a separate theory in one variable (first, and probably keep it forever). Complex analysis in one variables also has several features that simply do not exist in several variables.
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


Tracy Teal
is a co-founder and the Executive Director of Data Carpentry. As an Assistant Professor at Michigan State
University in bioinformatics, she saw that effective data skills have become foundational for research and that data training needs to scale along with data production. She is involved in the open source software
and reproducible research communities, including as an
Editor at the Journal for Open Source Software. Tracy tweets at
@tracykteal. Email: tkteal@datacarpentry.org


Defining the keyPressed() function in the sketch file will act as an event handler for key presses.
There are predifined variables in P5.js for certain keys. Watch how the position of the point moves when pressing the arrow keys.


|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object javax.swing.text.html.HTML.Tag
public static class HTML.Tag
Typesafe enumeration for an HTML tag. Although the set of HTML tags is a closed set, we have left the set open so that people can add their own tag types to their custom parser and still communicate to the reader.
| Constructor Summary | |
|---|---|
|
HTML.Tag()
|
protected |
HTML.Tag(String id)
Creates a new Tag with the specified id ,
and with causesBreak and isBlock
set to false . |
protected |
HTML.Tag(String id,
boolean causesBreak,
boolean isBlock)
Creates a new Tag with the specified id ;
causesBreak and isBlock are defined
by the user. |
| Method Summary | |
|---|---|
boolean |
breaksFlow()
Returns true if this tag causes a
line break to the flow of data, otherwise returns
false . |
boolean |
isBlock()
Returns true if this tag is a block
tag, which is a tag used to add structure to a
document. |
boolean |
isPreformatted()
Returns true if this tag is pre-formatted,
which is true if the tag is either PRE or
TEXTAREA . |
String |
toString()
Returns the string representation of the tag. |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Field Detail |
|---|
public static final HTML.Tag A
public static final HTML.Tag ADDRESS
public static final HTML.Tag APPLET
public static final HTML.Tag AREA
public static final HTML.Tag B
public static final HTML.Tag BASE
public static final HTML.Tag BASEFONT
public static final HTML.Tag BIG
public static final HTML.Tag BLOCKQUOTE
public static final HTML.Tag BODY
public static final HTML.Tag BR
public static final HTML.Tag CAPTION
public static final HTML.Tag CENTER
public static final HTML.Tag CITE
public static final HTML.Tag CODE
public static final HTML.Tag DD
public static final HTML.Tag DFN
public static final HTML.Tag DIR
public static final HTML.Tag DIV
public static final HTML.Tag DL
public static final HTML.Tag DT
public static final HTML.Tag EM
public static final HTML.Tag FONT
public static final HTML.Tag FORM
public static final HTML.Tag FRAME
public static final HTML.Tag FRAMESET
public static final HTML.Tag H1
public static final HTML.Tag H2
public static final HTML.Tag H3
public static final HTML.Tag H4
public static final HTML.Tag H5
public static final HTML.Tag H6
public static final HTML.Tag HEAD
public static final HTML.Tag HR
public static final HTML.Tag HTML
public static final HTML.Tag I
public static final HTML.Tag IMG
public static final HTML.Tag INPUT
public static final HTML.Tag ISINDEX
public static final HTML.Tag KBD
public static final HTML.Tag LI
public static final HTML.Tag LINK
public static final HTML.Tag MAP
public static final HTML.Tag MENU
public static final HTML.Tag META
public static final HTML.Tag NOFRAMES
public static final HTML.Tag OBJECT
public static final HTML.Tag OL
public static final HTML.Tag OPTION
public static final HTML.Tag P
public static final HTML.Tag PARAM
public static final HTML.Tag PRE
public static final HTML.Tag SAMP
public static final HTML.Tag SCRIPT
public static final HTML.Tag SELECT
public static final HTML.Tag SMALL
public static final HTML.Tag SPAN
public static final HTML.Tag STRIKE
public static final HTML.Tag S
public static final HTML.Tag STRONG
public static final HTML.Tag STYLE
public static final HTML.Tag SUB
public static final HTML.Tag SUP
public static final HTML.Tag TABLE
public static final HTML.Tag TD
public static final HTML.Tag TEXTAREA
public static final HTML.Tag TH
public static final HTML.Tag TITLE
public static final HTML.Tag TR
public static final HTML.Tag TT
public static final HTML.Tag U
public static final HTML.Tag UL
public static final HTML.Tag VAR
public static final HTML.Tag IMPLIED
This is a tag synthesized by the HTML reader. Since elements are identified by their tag type, we create a some fake tag types to mark the elements that were manufactured.
public static final HTML.Tag CONTENT
This is a tag synthesized by the HTML reader. Since elements are identified by their tag type, we create a some fake tag types to mark the elements that were manufactured.
public static final HTML.Tag COMMENT
This is a tag synthesized by the HTML reader. Since elements are identified by their tag type, we create a some fake tag types to mark the elements that were manufactured.
| Constructor Detail |
|---|
public HTML.Tag()
protected HTML.Tag(String id)
Tag
with the specified id
,
and with causesBreak
and isBlock
set to false
.
id
- the id of the new tagprotected HTML.Tag(String id, boolean causesBreak, boolean isBlock)
Tag
with the specified id
;
causesBreak
and isBlock
are defined
by the user.
id
- the id of the new tagcausesBreak
- true
if this tag
causes a break to the flow of dataisBlock
- true
if the tag is used
to add structure to a document| Method Detail |
|---|
public boolean isBlock()
true
if this tag is a block
tag, which is a tag used to add structure to a
document.
true
if this tag is a block
tag, otherwise returns false
public boolean breaksFlow()
true
if this tag causes a
line break to the flow of data, otherwise returns
false
.
true
if this tag causes a
line break to the flow of data, otherwise returns
false
public boolean isPreformatted()
true
if this tag is pre-formatted,
which is true if the tag is either PRE
or
TEXTAREA
.
true
if this tag is pre-formatted,
otherwise returns false
public String toString()
toString
in class Object
String
representation of the tag
|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
Copyright © 2004, 2010 Oracle and/or its affiliates. All rights reserved. Use is subject to license terms. Also see the documentation redistribution policy.


VSS Configuration Example¶
For Visual Source Safe you must specify the executable, project, username and password. You may also specify the SSDIR. If SSDIR is not set the default or the SSDIR environment variable will be used.
Version¶
Available from version 1.0
Examples¶
Minimal example
1<sourcecontrol type="vss" />
Full example
1<sourcecontrol type="vss">
2 <executable>C:\Program Files\Microsoft Visual Studio\VSS\win32\SS.EXE</executable>
3 <project>$/CCNET</project>
4 <username>buildguy</username>
5 <password>buildguypw</password>
6 <ssdir>c:\repos\</ssdir>
7 <applyLabel>false</applyLabel>
8 <autoGetSource>true</autoGetSource>
9 <alwaysGetLatest>false</alwaysGetLatest>
10 <workingDirectory>c:\myBuild</workingDirectory>
11 <culture>fr-FR</culture>
12 <cleanCopy>false</cleanCopy>
13 <timeout units="minutes">10</timeout>
14</sourcecontrol>
Configuration Elements¶
| Element | Description | Type | Required | Default | Version |
| type | The type of source control block. | String - must be vss | Yes | n/a | 1.0 |
| alwaysGetLatest | Specifies whether the most recent version of the source should be retrieved from VSS. If not, CCNet will obtain the source as of the time the build began. | Boolean | No | false | 1.0 |
| applyLabel | Specifies whether the current CCNet label should be applied to all source files under the current project in VSS. The specified VSS username must have write access to the repository. |
Boolean | No | false | 1.0 |
| autoGetSource | Specifies whether the current version of the source should be retrieved from VSS. | Boolean | No | true | 1.0 |
| cleanCopy | Controls whether or not VSS gets a clean copy (overwrites modified files) when getting the latest source. | Boolean | No | true | 1.0 |
| culture | The culture under which VSS is running. This value must match the culture of the VSS installation for CCNet to work with VSS. Most of the time the default is OK and you may omit this item. If you are using the US version of VSS on a machine that is not set to the US culture, you should include the configuration block <culture>en-US</culture>. | String | No | The culture of the VSS installation | 1.0 |
| dynamicValues | The dynamic values to use for the source control block. | Dynamic Values array | No | None | 1.5 |
| executable | The location of SS.EXE. If VSS is installed on the integration server, the location of VSS will be read from the registry and this element may be omitted. | String | No | None | 1.0 |
| issueUrlBuilder | Converts the comment (or parts from it) into an url pointing to the issue for this build. See IssueUrlBuilder for more details. | IssueUrlBuilder | No | None | 1.4 |
| password | Password for the VSS user ID. | PrivateString | No | None | 1.0 |
| project | The project in the repository to be monitored. | String | No | $/ | 1.0 |
| ssdir | The directory containing SRCSAFE.INI. If this SSDIR environment variable is already set then this property may be omitted. | String | No | None | 1.0 |
| timeout | Sets the timeout period for the source control operation. See Timeout Configuration for details. | Timeout Configuration | No | 10 minutes | 1.0 |
| username | VSS user ID that CCNet should use to authenticate. If the username is unspecified, the VSS client will attempt to authenticate using the NT user. | String | No | None | 1.0 |
| workingDirectory | The folder into which the source should be retrived from VSS. If this folder does not exist, it will be automatically created. | String | No | Project Working Directory | 1.0 |
Notes¶
Getting the latest source with VSS¶
VSS does not automatically remove files from the local workspace that have been deleted from the VSS database. This does not cause a problem if you are using the <solution> task or the Visual Studio Task to compile your project. However, if you are packaging the source for deployment or if you are using the <csc> task to produce a custom build, you may end up compiling these deleted files in your assembly. To be on the safe side, it might be a good idea to clear the contents of the local workspace after each build.
Using a US English VSS in a non-English culture¶
If you use an English VSS with machines configured to use a non-English culture, it may happen that CCNet will not detect any modifications after you check-in some code. The reason for this behaviour is that CCNet uses the selected culture on the build server to determine the language it expects VSS will output for parsing. For example, with fr-CA, CCNet looks for French keywords in the VSS output. Hence, if your VSS installation does not use the same language, CCNET will not be able to detect any modification.
If you're using a US VSS installation, the first step in solving this problem is to include a configuration block set to the US english culture (<culture>en-US</culture>). This will make CCNet look for English VSS keywords, and eventually detect modifications.
VSS Issues¶
CCNet periodically reports the following error when connecting to VSS: "Unable to open user login file \SourceSafe\Vss60\data\loggedin\<userid>.log." What gives?
If you have set CCNet up to manage multiple projects that all connect to the VSS repository using the same user id then you may sporadically receive this failure. Our analysis suggests that the root of the problem is caused by the fact that VSS will create the <userid>.log file when a user logs into VSS and delete it when the user logs out again. If a second build is using the repository concurrently with the same user, when that second build logs out it looks for <userid>.log, but it's gone. Hence the error.
There are three approaches that you can take to deal with this:
* Log into VSS using different users for each CCNet project. * You can keep CCNet from publishing exceptions , so this exception will just get logged into the ccnet.log file. * Leave the VSS GUI open on the integration server. This will mean the userid.log file never gets deleted.
p.
If you're using a labeller that returns a label equal to one already applied in the repository, the old label will be deleted when the new one is added.
This is because of a quirk in how VSS deals with labels of the same name; it should not be a problem with the default labeller.
This problem usually occurs when someone is using a custom labeller (a class that implements ILabeller) and that custom labeller returns a constant value.
Workaround: If you use a custom labeller, make sure each label is unique.
When I try to connect to use the <vss> NAntContrib tasks from The Server Service Application I get this error: Failed to open database \\someserver\someshare\vssrep\srcsafe.ini
There are a number of known issues with SourceSafe 6.0c. Make sure that you upgrade to the 6.0d version.
When I try connecting to VSS when using The Server Service Application I get the error: No VSS database (srcsafe.ini) found. Use the SSDIR environment variable or run netsetup.
Make sure that you are running ccservice using an account that has the necessary permissions to access the network share where your VSS database is set up. By default ccservice will run as the LocalSystem account, which does not have the necessary priviledges to access other machines.
When using VSS with a Filtered Source Control Block, newly added or removed files don't show up as modifications
VSS does not output the paths for added or deleted files. As a result, the modifications returned by CCNet do not have any specified path information. If a Filtered Source Control Block is used with a path filter then these modifications are likely to be filtered out. This is an outstanding issue.
Useful links¶
* Visual SourceSafe Best Practices Guide - http://msdn.microsoft.com/library/default.asp?url=/library/en-us/dnvss/html/vssbest.asp * Using VSS With Multiple Timezones - http://support.microsoft.com/default.aspx?scid=kb;en-us;248240&Product=vss * OLE Automation interface Get method behaves differently with VSSVersion and with VSSItem - http://support.microsoft.com/default.aspx?scid=kb;en-us;837417
Automatically Generated¶
Documentation generated on Saturday, 30 Jun 2012 at 6:00:14 AM


This is an example application demonstrating how to embed Pentaho Reporting into your web application.


{% extends "global/Page.html" %} {% load otree static %} {% block title %} INTRODUCTION - PLEASE READ CAREFULLY {% endblock %} {% block content %}
Welcome! We want to thank you all for accepting this invitation and participating in this exercise. The objective of this exercise is to understand how people make decisions related to the use of a shared natural resource.
A record of your participation in this research study will be maintained, but this record will be kept strictly confidential. Further, no one (including the experimenters) will be able to associate your responses in today’s study with you personally. Responses will only be attributable to your unique ID.
Your participation in this study will take approximately 1 hour.
Your participation in this study is voluntary. You do not have to answer any question that you do not want to answer for any reason. However, if you withdraw from the study before you have reached the end, you will not receive payment. If you have any questions or concerns about the project, you may contact the researcher, Botao Qin, at<PHONE_NUMBER> or<EMAIL_ADDRESS>Continuing on with the study implies your consent to participate. If you agree to participate, please click the “Agree” button below to continue.


mixins.graph-graphic-with-tracking-dot Class
Adds tracking dot functionality to a component such as components.nf-line or components.nf-area
Item Index
Methods
Properties
Methods
_initializeTrackingDot
()
private
Sets up subscriptions to content hover events.
_sendDidTrack
()
private
Observes changes to tracked data and sends the didTrack action.
_sendWillTrack
()
private
Observes impending changes to trackedData and sends the willTrack action.
_teardownTrackingDot
()
private
Tears down subscriptions to content hover events.
didContentHoverChange
-
e
Event handler for content hoverChange event. Triggers didHoverChange
.
Parameters:
didContentHoverEnd
-
e
Handles the graph-content's hoverEnd event and triggers didHoverEnd
Parameters:
-
e
utils.nf.graph-mouse-action-context
didHoverChange
-
e
Event handler for didHoverChange. Sends hoverChange action.
Parameters:
didHoverEnd
-
e
Event handler for didHoverEnd. Updates tracked data, and sends hoverEnd action.
Parameters:
-
e
Objecthover end event object.
Properties
didTrack
String
The action to send on didTrack
.
Default: null
hoverChange
String
The action name to send to the controller when the hoverChange
event fires
Default: null
hoverEnd
String
Name of the action to send on hoverEnd
Default: null
trackingMode
String
Gets or sets the tracking mode of the component.
Possible values are:
- 'none': no tracking behavior
- 'hover': only track while mouse hover
- 'snap-last': track while mouse hover, but snap to the last data element when not hovering
- 'snap-first': track while mouse hover, but snap to the first data element when not hovering
- 'selected-hover': The same as
'hover'
tracking mode, but only when the compononent is selected - 'selected-snap-last': The same as
'snap-last'
tracking mode, but only when the compononent is selected - 'selected-snap-first': The same as
'snap-first'
tracking mode, but only when the compononent is selected
Default: 'none'
willTrack
String
The action to send on willTrack
Default: null


#include <stringizer.hpp>
Collaboration diagram for iof::stringizer:
E.g.:
std::string name = iof::stringizer("%s, %s") & first & last;
This does the same as the tostr() function, but using same syntax as iof::fmtr.
Public Member Functions | |
| stringizer (const std::string &fmt) | |
| stringizer (const stringizer &rhs) | |
| bool | operator== (const stringizer &rhs) const |
| bool | operator!= (const stringizer &rhs) const |
| template<typename TT> | |
| stringizer & | operator<< (const TT &obj) |
| template<typename TT> | |
| stringizer & | operator & (const TT &obj) |
| operator std::string () | |
| std::string | fmt () const |


Bring the innovative minds spread all across the country under a single canopy, where they can showcase their talent, share the knowledge and inspire millions more to explore the possible dimensions of their respective field. This event would also be a phenomenal opportunity for the students to showcase their ideas and/or implementations as a cherishing outcome in the contribution to the Nation's Dream of all current government programs like Digital India, Make in India, Skill India, Smart City, Swachh Bharat Abhiyan.
Have any query? Feel free to contact us.
Indore – Ujjain Road
Indore – 453111
+91<PHONE_NUMBER>
+91<PHONE_NUMBER>


Before Change
with open("requirements.txt", encoding="utf-8") as requirements:
requires = [l.strip() for l in requirements]
with open("README.rst", encoding="utf-8") as readme_f:
readme = readme_f.read()
author = ", ".join([
"Alex Wolf",
"Philipp Angerer",
"Fidel Ramirez",
After Change
import sys
if sys.version_info < (3,):
sys.exit("scanpy requires Python >= 3.5")
from pathlib import Path
from setuptools import setup, find_packages
import versioneer


The output Q is set by the input S, is reset by the input R, and keeps its value in between. QI is the inverse of Q.
Q
S
R
QI


Student Name: Date of Birth:
Gender: Male Female Other
Father Name: Mother Name:
Father Contact: Mother Contact:
Father CNIC: Mother CNIC:
Father Email: Mother Email:
Father Address: Mother Address:
Guardian Name:
Guardian Contact:
Guardian CNIC:
Guardian Relation:
Fee Amount:
Discount:
Final Amount:
Fee Fully Paid: YES NO
Challan Number:


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slices between L and R locators
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Midi start note:
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C#
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A
A#
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1
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Use the chromatic scale
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Available notes:
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Estimated BPM:
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Open
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Threshold
45
Nudge all
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Tempo
1x
Pitch
0
Envelope
Attack
0ms
Hold
0ms
Decay
∞
Compressor
Threshold
0db
Ratio
1:1
Attack
70ms
Release
100ms
Gain
0dB
Low
Freq
250Hz
Gain
0db
Mid
Freq
1000Hz
Gain
0db
Q
1
High
Freq
4000Hz
Gain
0db
About this app:


Changelog¶
1.2.23 (27 July 2020)¶
Bug fixes¶
fixed padded block writing with user functions in
geowombat.to_raster()
.Added check for existing metadata file in
geowombat.util.web.download_cube()
.
Enhancements¶
Reorganized the ReadTheDocs pages.
1.2.22 (21 July 2020)¶
Bug fixes¶
Added missing ‘l5’ download flag in
geowombat.util.web.download_cube()
.
Enhancements¶
Added ‘l5’ to
geowombat.radiometry.sr.bandpass()
.Modified support for writing GeoTiffs as separate files in
geowombat.to_raster()
.The previous version used the DataArray.transform property, which was derived from the full raster extent. The latest version of GeoWombat uses a DataArray.gw.transform, which is an updated transform property for each raster chunk.
1.2.21 (8 July 2020)¶
Bug fixes¶
Added check for None row/column chunks in
geowombat.core.parallel.ParallelTask
.
Enhancements¶
Added affine and transform properties.
1.2.20 (7 July 2020)¶
Bug fixes¶
Fixed conditional value replacement in
geowombat.calc_area()
.
Enhancements¶
Added return_binary argument to
geowombat.core.geoxarray.GeoWombatAccessor.compare()
.
New¶
Created
geowombat.core.parallel.ParallelTask
.
1.2.19 (6 July 2020)¶
Enhancements¶
Added source attributes to return object in
geowombat.core.geoxarray.GeoWombatAccessor.compare()
.
New¶
Created
geowombat.core.geoxarray.GeoWombatAccessor.replace()
function.Created
geowombat.replace()
function.Created
geowombat.core.geoxarray.GeoWombatAccessor.recode()
function.Created
geowombat.recode()
function.
1.2.18 (1 July 2020)¶
Bug fixes¶
Fixed call to
geowombat.sample()
fromDataArray
method.
New¶
Added image metadata tags to
DataArray
attributes ingeowombat.open
.Added support for VRT creation from multiple files.
Created
geowombat.calc_area()
function.Created
geowombat.core.geoxarray.GeoWombatAccessor.compare()
function.Created
geowombat.core.geoxarray.GeoWombatAccessor.match_data()
function.
1.2.17 (25 June 2020)¶
Bug fixes¶
Added missing tag update in file compression stage.
Fixed issue with compression being triggered with
compress=None
orcompress=False
.
1.2.16 (22 June 2020)¶
Bug fixes¶
Fixed an issue with lingering configuration reference bounds.
New¶
Added metadata tags keyword argument to
geowombat.to_raster()
.Added chunk_grid and footprint_grid DataArray properties.
1.2.15 (15 June 2020)¶
New¶
Added
set_nodata()
function for DataArrays.Added
bounds_overlay()
function for DataArrays.
1.2.14 (12 June 2020)¶
Bug fixes¶
Fixed ‘no data’ clipping error in
geowombat.util.GeoDownloads.download_cube()
.
New¶
Added file_list to
geowombat.core.sort_images_by_date()
.Added nodata keyword argument to
geowombat.open
.
1.2.13 (2 June 2020)¶
New¶
Added cloud_height option in
download_cube()
.Added first attempt at supporting HDF reads.
1.2.12 (3 May 2020)¶
New¶
Added
geowombat.radiometry.angles.estimate_cloud_shadows()
to estimate cloud shadows from a cloud mask.
Bug fixes¶
Fixed month range parsing in
geowombat.util.web.download_cube()
.
1.2.10 (27 April 2020)¶
New¶
Added support for
rasterio.windows.Window()
andrasterio.coords.BoundingBox()
objects in thegeowombat.config.update()
manager.
1.2.9 (1 April 2020)¶
Removed f-string requirement in setup.py script.
Added keyword argument in
download_cube()
function.
1.2.8 (1 April 2020)¶
Modified sample iteration overhead in
geowombat.moving.moving_window()
.
New¶
Added weights option in
geowombat.moving.moving_window()
.
1.2.7 (31 March 2020)¶
New¶
Added window weights for moving mean.
Changed
geowombat.moving.moving_window()
parallelism from raster rows to raster samples.
1.2.6 (15 March 2020)¶
Fixed missing gw.filename attribute in
geowombat.to_vrt()
.
1.2.5 (4 March 2020)¶
Added attribute updating for band math.
1.2.4 (26 February 2020)¶
Added CRS check for pyproj CRS instances.
1.2.3 (23 February 2020)¶
Added check to support new CRS object in geowombat.
Added padding to image edges when using the padding option in
geowombat.to_raster()
.Added checks for empty CRS objects.
Added the Advanced Vegetation Index.
Added
geowombat.core.lonlat_to_xy()
andgeowombat.core.xy_to_lonlat()
functions.
1.2.2 (12 February 2020)¶
Added padding option to
geowombat.to_raster()
.Added half cell adjustment to transformed samples in
geowombat.sample()
.
1.2.1 (12 February 2020)¶
Fixed a error in checking the spatial index.
1.2.0 (11 February 2020)¶
Rearranged and renamed various functions.
geowombat.to_crs()
is deprecated in favor ofgeowombat.transform_crs()
.geowombat.geodataframe_to_array()
is deprecated in favor ofgeowombat.polygon_to_array()
.geowombat.to_geodataframe()
is deprecated in favor ofgeowombat.array_to_polygon()
.
Added lazy_wombat decorator to allow the user to apply in-memory functions lazily. See User functions for examples.
1.1.6 (9 February 2020)¶
Added new property geodataframe.
Fixed error with pass bounds_by argument option to
mosaic()
.Modified
geowombat.to_crs()
to handle grid resampling.
1.1.5 (7 February 2020)¶
Fixed error that caused the deletion of ref_tar when multiple raster files were opened.
1.1.4 (7 February 2020)¶
Added configuration option ref_tar to target align outputs to a reference raster. Example usage looks like:
# Subset a raster but align to a target grid
with gw.config.update(ref_bounds=bounds, ref_tar='image.tif'):
with gw.open() as src:
...
1.1.3 (6 February 2020)¶
Added new function
geowombat.geodataframe_to_array()
to convert a geopandas.GeoDataFrame to an xarray.DataArray.
1.1.2 (5 February 2020)¶
Added ‘empirical-rotation’ method to
geowombat.Topo().norm_topo()
function ingeowombat.Topo
1.1.1 (28 January 2020)¶
Fixed error reading by a extent bounds with dask.delayed
1.1.0 (24 January 2020)¶
Added new class
geowombat.radiometry.Topo
for topographic corrections.Added new xarray.DataArray geowombat accessor
to_crs()
for CRS transformations.Added new function
geowombat.core.sort_images_by_date()
.Added geowombat.radiometry module to the documentation.
Added new xarray.DataArray geowombat property bounds_as_namedtuple.
Rearranged documentation and fixed minor docstring issues.
1.0.7 (23 January 2020)¶
Added new functions
geowombat.core.indices_to_coords()
andgeowombat.core.coords_to_indices()
to replacegeowombat.core.ij_to_xy()
andgeowombat.core.xy_to_ij()
.
1.0.6 (21 January 2020)¶
Added missing imports for
geowombat.sample()
.
1.0.5 (21 January 2020)¶
Changes¶
Modified
geowombat.sample()
. New functionality includes:Systematic sampling
Random sampling
Stratified random sampling
1.0.4 (19 January 2020)¶
Changes¶
Removed DataArray list option from
geowombat.coregister()
.
Bug fixes¶
Fixed an error with global configuration settings that occurred when ref_image was used and subsequently overwritten.
Removed band_names argument from
imshow()
.
1.0.3 (17 January 2020)¶
Bug fixes¶
Added workaround example in the documentation for
geowombat.moving()
.
1.0.2 (16 January 2020)¶
Bug fixes¶
Fixed a problem with
geowombat.moving()
block overlaps when requested window sizes were larger than the smallest Dask chunk size.Fixed
geowombat.moving()
percentile quantile sorting of a full moving window.
1.0.1 (15 January 2020)¶
New¶
Added a check for even or odd window sizes with
geowombat.moving()
.Added an option to co-register a list of images.
Added percentiles to
geowombat.moving()
.
Bug fixes¶
Fixed missing DataArray attributes after changing data type.
1.0.0 (13 January 2020)¶
First release
1.3.7b (12 January 2020)¶
New¶
Added
geowombat.radiometry.pan_sharpen()
function.Added properties for multi-spectral + panchromatic band stacks.
1.3.0b (9 January 2020)¶
New¶
Added
geowombat.to_vrt()
function.
1.2.0b (29 December 2019)¶
New¶
Added
geowombat.to_geodataframe()
function.
Bug fixes¶
Fixed GeoDataFrame CRS check in
geowombat.extract()
function.
1.0.7b (20 December 2019)¶
New¶
Added user argument dtype in
geowombat.open
.
Bug fixes¶
Fixed time and band stacking error.
Fixed dictionary string name error in CRF feature processing
1.0.0b (27 November 2019)¶
New¶
Added
geowombat.mask()
function.
Bug fixes¶
Fixed row/column offset error with
warp()
function.
1.0.0b (1 November 2019)¶
Enhancements¶
Added expand_by user argument in
geowombat.clip()
.
1.0.0b (30 October 2019)¶
New¶
Added user functions as Xarray attributes. See
geowombat.apply()
for an example.
1.0.0b (24 October 2019)¶
Enhancements¶
Implemented improvements from testing processes vs. threads for concurrent I/O in
geowombat.to_raster()
.
Bug fixes¶
Changed BRDF normalization (
geowombat.radiometry.BRDF
) from 1d to 2d in order to work with Dask arrays.
1.0.0b (23 October 2019)¶
Changes¶
Added fixes for surface reflectance
New¶
Added support for band stacking (in addition to time stacking) in
geowombat.open
. The new keyword argument is stack_dim and can be used like:
with gw.open(..., stack_dim='band') as ds:
...


Text info database member specified by several parameters.
More...
|
| def | __init__ (self, str text_id, bool has_chunks, str local_path, str chunk_separator="\n", str url="") |
| | constructor for text info More...
|
| |
|
def | __str__ (self) |
| | string representation of texts
|
| |
| def | save (self) |
| | save text info to text info db More...
|
| |
| def | from_text_id (cls, str text_id) |
| | create text info object using text id for documents inside database More...
|
| |
| def | from_info (cls, dict info, str text_id) |
| | create text info object from info dict and text identifier More...
|
| |
|
|
| text_id |
| |
|
| has_chunks |
| |
|
| local_path |
| |
|
| chunk_separator |
| |
|
| url |
| |
Text info database member specified by several parameters.
◆ __init__()
| def agsearch.textinfo.TextInfo.__init__ |
( |
|
self, |
|
|
str |
text_id, |
|
|
bool |
has_chunks, |
|
|
str |
local_path, |
|
|
str |
chunk_separator = "\n" , |
|
|
str |
url = "" |
|
) |
| |
constructor for text info
- Parameters
-
| text_id | identifier of the given text |
| has_chunks | specifies whether a given text has more than one chunk |
| local_path | local path to the text document |
| chunk_separator | separator of chunks for document. |
| url | specifies the url of text if it comes from web. |
◆ from_info()
| def agsearch.textinfo.TextInfo.from_info |
( |
|
cls, |
|
|
dict |
info, |
|
|
str |
text_id |
|
) |
| |
create text info object from info dict and text identifier
- Parameters
-
| info | dictionary that contains dictionary representation of the given text object. |
| text_id | identifier of the text object |
◆ from_text_id()
| def agsearch.textinfo.TextInfo.from_text_id |
( |
|
cls, |
|
|
str |
text_id |
|
) |
| |
create text info object using text id for documents inside database
- Parameters
-
| text_id | identifier of documents |
◆ save()
| def agsearch.textinfo.TextInfo.save |
( |
|
self | ) |
|
save text info to text info db
We create a dictionary representation of the current text info member. Then save it to text id.
The documentation for this class was generated from the following file:


{% load staticfiles %}
Shared pagination Page
{% include "myapp/share_include.html" with url_param="my:share" %}
title
content
{% for article in object_list %}
{{ article.title }}
{{ article.content }}
{% endfor %}
{% include "myapp/share_include.html" with url_param="my:share" %}


Academy:
Faculty:
Lectuerer:
Course:
Semester:
Reviewers number:
Course level (Avg):
Lecturer atitude(Avg):
Motivation to teach (Avg):
Lecturer Intersting (Avg):
| # |
Reviewer |
Rank Date |
Course level |
Lecturer atitude |
Motivation to teach |
Lecturer Intersting |
Few words |


Port of the amazing Bootstrap 3.3.4 to Stylus 0.47.0.
There might be some slight color differences due to the differences between the color functions in LESS and those in Stylus.
Latest version via Bower:
$ bower install bootstrap-stylus
Legacy 2.3.2 version is kept in this release and can be installed via Bower as well, however it does not include any build scripts.
$ bower install bootstrap-stylus#2.3.2
Via npm:
$ npm install bootstrap-styl
Example of requiring and using bootstrap middleware to create compile function to utilize in other frameworks.
var bootstrap = require('bootstrap-styl'),
stylus = require('stylus');
function compile(str) {
return stylus(str)
.use(bootstrap());
}
To import whole bootstrap in your stylus file:
@import bootstrap
Alternatively to import individual components:
@import 'bootstrap/alerts'
@import 'bootstrap/variables'
@import 'bootstrap/mixins'
Bootstrap Stylus uses Grunt with convenient methods for working with the framework. To use it, install the required dependencies as directed and then run some Grunt commands.
From the command line:
grunt-cli
globally with npm install -g grunt-cli
.npm install
When completed, you'll be able to run the various Grunt commands provided from the command line.
Unfamiliar with npm
? Don't have node installed? That's a-okay. npm stands for node packaged modules and is a way to manage development dependencies through node.js. Download and install node.js before proceeding.
grunt dist
grunt dist
creates the /dist
directory with compiled files. Uses UglifyJS.
grunt theme
This will compile the theme.styl
separately and output it to the /dist
folder.
grunt watch
This is a convenience method for watching just Stylus files and automatically building them whenever you save.
Should you encounter problems with installing dependencies or running Grunt commands, uninstall all previous dependency versions (global and local). Then, rerun npm install
.
stylus/bootstrap.styl
.// myStyles.styl
$font-family-serif ?= 'Merriweather', serif
$font-family-base ?= $font-family-serif
@import "bower_components/bootstrap-stylus/stylus/bootstrap.styl" // Or wherever your bootstrap.styl is located
this allows flexibility for easily updating to new bootstrap versions. See ?=.gulpfile.js
. You can ommit components by removing them from the uglify:dist:files
list.Mark Otto
Jacob Thornton
Copyright 2013 Twitter, Inc under the Apache 2.0 license.


Working groups job dashboard:
This page summarizes recent task and job status for working groups. The working groups shown are (only) those with tasks and jobs in the system during the covered interval. The working group names come from the name specified in task and job definitions, as recorded in the workinggroup field in task and job DB tables.
Failure rates above {% if errthreshold %}{{ errthreshold }}% {% else %}a threshold {% endif %} are shown in bold red. Click the task, job counts to go to task, job listings. Click on the failure percentage to go to an error summary. For task state documentation see this.


Meet the Cats!
Josie
Manager
ID: undefined
Email:
Phone Number: 1234567
Joe
Engineer
ID: undefined
Email:
GitHub:
JoeJoe
Nibbles
Engineer
ID: undefined
Email:
GitHub:
Nibbleses
Spicy
Intern
ID: undefined
Email:
School: Spicy U


- Short Description
- The morula (1) continues to undergo cell divisions. As it does, cells start to migrate into separate layers, and a cavity starts to develop inside the ball of cells. When cells have migrated into distinct layers, the organism is called a blastocyst (2)
- Long Description
- Further information about this "Blastulation" image is available at Wikipedia (Public Domain).


This web app has a simple front end UI through javascript to the backend ToDo API that it hosts.
There are two API Controllers that hosts:
- /api/values = This controller has some demo api operations AND the Hybrid operations
- /api/values (get all values)
- /api/values/id (get by int id)
- /api/values/devicerecords (get all records over hybrid)
- /api/values/logsn/SerialNumber (update a record over hybrid)
- /api/todo = This controller has demo api operations used by this web page (below)
[Optional Note: you need to set the web app URL in the Javascript variable apiendpoint in this index.html file for the UI below to work]
- /api/values (get all values)
- /api/values/id (get by int id)
- /api/values/devicerecords (get all records over hybrid)
- /api/values/logsn/SerialNumber (update a record over hybrid)
[Optional Note: you need to set the web app URL in the Javascript variable apiendpoint in this index.html file for the UI below to work]


{%extends 'base.html'%} {%load bootstrap4%} {{block.super}} {%block content%} {%for i in projects%}
by {{i.user}} with a Rating of {{average}}
Thanks for rating This app
{{project.description}}
Votes by {{all|length}} users
{%if all%}
Voters
Rating
{%for us in all%}
{{us.user}}
{{us.design}}.0
{{us.usability}}.0
{{us.content}}.0
{%endfor%}
{%else%}
- No votes Available
{%endif%}


class: center, middle, inverse, title-slide # ETC3250: Dimension reduction ## Semester 1, 2019 ###
Professor Di Cook
Econometrics and Business Statistics
Monash University ### Week 4 (a) --- class: middle background-image: url(https://upload.wikimedia.org/wikipedia/commons/9/98/Andromeda_Galaxy_%28with_h-alpha%29.jpg) background-position: 50% 50% class: center, bottom, inverse .white[Space is big.  I mean, you may think it's a long way down the road to the chemist's, but that's just peanuts to space.] *.white[Douglas Adams, Hitchhiker's Guide to the Galaxy]* --- class: split-30 layout: false .column[.pad10px[ ## Outline - .green[High dimensions] - Definition ]] .column[.top50px[

 Remember, our data can be denoted as: `\(\mathcal{D} = \{(x_i, y_i)\}_{i = 1}^N, ~~~ \mbox{where}~ x_i = (x_{i1}, \dots, x_{ip})^{T}\)`

 then .boxshadow[.orange[.content[Dimension]] .content[of the data is *p*,] .orange[.content[the number of variables.]]] ]] --- class: split-30 layout: true .column[.pad10px[ ## Outline - .green[High dimensions] - Definition - Cubes and spheres ]] .column[.top50px[ .row[.content[ Space expands exponentially with dimension:  ]] .row[.content[ As dimension increases the .orange[volume of a sphere] of same radius as cube side length becomes much .orange[smaller than the volume of the cube]:  ]] ]] --- class: fade-row2-col2 --- count: false --- class: split-30 layout: true .column[.pad10px[ ## Outline - .green[High dimensions] - Definition - Cubes and spheres - Sub-spaces ]] .column[.top50px[ .row[.content[

 Data will often be confined to a region of the space having lower .orange[intrinsic dimensionality]. The data lives in a low-dimensional subspace.

 ]] .row[.content[ .orange[SO, reduce dimensionality], to the subspace containing the data. ]] ]] --- class: fade-row2-col2 --- count: false --- class: split-30 layout: true .column[.pad10px[ ## Outline - High dimensions - .green[PCA] - Definition ]] .column[.top50px[ .row[.content[ .boxshadow[ .content[ Principal component analysis (PCA) produces a low-dimensional representation of a dataset. It finds a sequence of linear combinations of the variables that have .orange[maximal variance], and are .orange[mutually uncorrelated]. It is an unsupervised learning method. ]] ]] .row[.content[ Why? - We may have too many predictors for a regression. Instead, we can use the first few principal components. - Understanding relationships between variables. - Data visualization. We can plot a small number of variables more easily than a large number of variables. ]] ]] --- class: fade-row2-col2 --- count: false --- class: split-30 layout: true .column[.pad10px[ ## Outline - High dimensions - .green[PCA] - Definition ]] .column[.top50px[ .row[.content[ ### First principal component The first principal component of a set of variables `\(x_1, x_2, \dots, x_p\)` is the linear combination .boxshadow[.content[ `$$z_1 = \phi_{11}x_1 + \phi_{21} x_2 + \dots + \phi_{p1} x_p$$` ]] that has the largest variance such that .content[ `$$\displaystyle\sum_{j=1}^p \phi^2_{j1} = 1$$` ] ]] .row[.content[ The elements `\(\phi_{11},\dots,\phi_{p1}\)` are the .orange[loadings] of the first principal component. ]] ]] --- class: fade-row2-col2 --- count: false --- class: split-30 layout: true .column[.pad10px[ ## Outline - High dimensions - .green[PCA] - Definition - Geometry ]] .column[.top50px[ .row[.content[ - The loading vector `\(\phi_1 = [\phi_{11},\dots,\phi_{p1}]'\)` defines direction in feature space along which data vary most. ]] .row[.content[ - If we project the `\(n\)` data points `\({x}_1,\dots,{x}_n\)` onto this direction, the projected values are the principal component scores `\(z_{11},\dots,z_{n1}\)`. ]] .row[.content[ - The second principal component is the linear combination `\(z_{i2} = \phi_{12}x_{i1} + \phi_{22}x_{i2} + \dots + \phi_{p2}x_{ip}\)` that has maximal variance among all linear combinations that are *uncorrelated* with `\(z_1\)`. ]] .row[.content[ - Equivalent to constraining `\(\phi_2\)` to be orthogonal (perpendicular) to `\(\phi_1\)`. And so on. ]] .row[.content[ - There are at most `\(\min(n - 1, p)\)` PCs. ]] ]] --- class: fade-row2-col2 fade-row3-col2 fade-row4-col2 fade-row5-col2 --- class: fade-row3-col2 fade-row4-col2 fade-row5-col2 count: false --- class: fade-row4-col2 fade-row5-col2 count: false --- class:fade-row5-col2 count: false --- count: false --- class: split-30 layout: false .column[.pad10px[ ## Outline - High dimensions - .green[PCA] - Definition - Geometry - Example ]] .column[.top50px[    .green[First PC]; .blue[second PC] .font_tiny[(Chapter6/6.14.pdf)] ]] --- class: split-30 layout: false .column[.pad10px[ ## Outline - High dimensions - .green[PCA] - Definition - Geometry - Example ]] .column[.top50px[    If you think of the first few PCs like a linear model fit, and the others as the error, it is like regression, except that errors are orthogonal to model. .font_tiny[(Chapter6/6.15.pdf)] ]] --- class: split-30 layout: false .column[.pad10px[ ## Outline - High dimensions - .green[PCA] - Definition - Geometry - Example - Computation ]] .column[.top50px[ PCA can be thought of as fitting an `\(n\)`-dimensional ellipsoid to the data, where each axis of the ellipsoid represents a principal component. The new variables produced by principal components correspond to .orange[rotating] and .orange[scaling] the ellipse .orange[into a circle].  ]] --- class: split-30 layout: false .column[.pad10px[ ## Outline - High dimensions - .green[PCA] - Definition - Geometry - Example - Computation ]] .column[.top50px[ Suppose we have a `\(n\times p\)` data set `\(X = [x_{ij}]\)`. - Centre each of the variables to have mean zero (i.e., the column means of `\({X}\)` are zero). - `\(z_{i1} = \phi_{11}x_{i1} + \phi_{21} x_{i2} + \dots + \phi_{p1} x_{ip}\)` - Sample variance of `\(z_{i1}\)` is `\(\displaystyle\frac1n\sum_{i=1}^n z_{i1}^2\)`. `$$\mathop{\text{maximize}}_{\phi_{11},\dots,\phi_{p1}} \frac{1}{n}\sum_{i=1}^n \left(\sum_{j=1}^p \phi_{j1}x_{ij}\right)^{\!\!\!2} \text{ subject to } \sum_{j=1}^p \phi^2_{j1} = 1$$` ]] --- class: split-30 layout: false .column[.pad10px[ ## Outline - High dimensions - .green[PCA] - Definition - Geometry - Example - Computation ]] .column[.top50px[ 1. Compute the covariance matrix (after scaling the columns of `\({X}\)`) `$${C} = {X}'{X}$$` 2. Find eigenvalues and eigenvectors: `$${C}={V}{D}{V}'$$` where columns of `\({V}\)` are orthonormal (i.e., `\({V}'{V}={I}\)`) 3. Compute PCs: `\({\Phi} = {V}\)`. `\({Z} = {X}{\Phi}\)`. ]] --- class: split-30 layout: false .column[.pad10px[ ## Outline - High dimensions - .green[PCA] - Definition - Geometry - Example - Computation ]] .column[.top50px[ Singular Value Decomposition `$$X = U\Lambda V'$$` - `\(X\)` is an `\(n\times p\)` matrix - `\(U\)` is `\(n \times r\)` matrix with orthonormal columns ( `\(U'U=I\)` ) - `\(\Lambda\)` is `\(r \times r\)` diagonal matrix with non-negative elements. - `\(V\)` is `\(p \times r\)` matrix with orthonormal columns ( `\(V'V=I\)` ). .boxshadow[.content[ It is always possible to uniquely decompose a matrix in this way. ]] ]] --- class: split-30 layout: false .column[.pad10px[ ## Outline - High dimensions - .green[PCA] - Definition - Geometry - Example - Computation ]] .column[.top50px[ 1. Compute SVD: `\({X} = {U}{\Lambda}{V}'\)`. 2. Compute PCs: `\({\Phi} = {V}\)`. `\({Z} = {X}{\Phi}\)`. Relationship with covariance: `$${C} = {X}'{X} = {V}{\Lambda}{U}' {U}{\Lambda}{V}' = {V}{\Lambda}^2{V}' = {V}{D}{V}'$$` - Eigenvalues of `\({C}\)` are squares of singular values of `\({X}\)`. - Eigenvectors of `\({C}\)` are right singular vectors of `\({X}\)`. - The PC directions `\(\phi_1,\phi_2,\phi_3,\dots\)` are the right singular vectors of the matrix `\({X}\)`. ]] --- class: split-30 layout: true .column[.pad10px[ ## Outline - High dimensions - .green[PCA] - Definition - Geometry - Example - Computation - Total variance ]] .column[.top50px[ .row[.content[ .orange[Total variance] in data (assuming variables centered at 0): `$$\text{TV} = \sum_{j=1}^p \text{Var}(x_j) = \sum_{j=1}^p \frac{1}{n}\sum_{i=1}^n x_{ij}^2$$` ]] .row[.content[ .boxshadow[.orange[.content[ If variables are standardised, TV=number of variables! ]]] ]] .row[.content[ .orange[Variance explained] by *m*'th PC: .content[ `$$V_m = \text{Var}(z_m) = \frac{1}{n}\sum_{i=1}^n z_{im}^2$$` `$$\text{TV} = \sum_{m=1}^M V_m \text{ where }M=\min(n-1,p).$$` ]]] ]] --- class: fade-row2-col2 fade-row3-col2 --- class:fade-row3-col2 count: false --- count: false --- class: split-30 layout: false .column[.pad10px[ ## Outline - High dimensions - .green[PCA] - Definition - Geometry - Example - Computation - Total variance - Choosing `\(k\)` ]] .column[.top50px[ .boxshadow[.orange[.content[ Proportion of variance explained:]] .content[ `$$\text{PVE}_m = \frac{V_m}{TV}$$` ]] Choosing the number of PCs that adequately summarises the variation in X, is achieved by examining the cumulative proportion of variance explained. Cumulative proportion of variance explained: .content[ `$$\text{CPVE}_k = \sum_{m=1}^k\frac{V_m}{TV}$$` ] and also by a scree plot. ]] --- class: split-30 layout: false .column[.pad10px[ ## Outline - High dimensions - .green[PCA] - Definition - Geometry - Example - Computation - Total variance - Choosing `\(k\)` ]] .column[.top50px[ .boxshadow[ .orange[.content[Scree plot: ]].content[Plot of variance explained by each component vs number of component.]]  ]] --- class: split-30 layout: false count: false .column[.pad10px[ ## Outline - High dimensions - .green[PCA] - Definition - Geometry - Example - Computation - Total variance - Choosing `\(k\)` ]] .column[.top50px[ .boxshadow[ .orange[.content[Scree plot: ]].content[Plot of variance explained by each component vs number of component.]]  ]] --- class: split-30 layout: false .column[.pad10px[ ## Outline - High dimensions - PCA - .green[Example] - Data ]] .column[.top50px[ The data on national track records for women (as at 1984). ``` ## Observations: 55 ## Variables: 8 ## $ m100 <dbl> 11.61, 11.20, 11.43, 11.41, 11.46, 11.31, 12.14, 11.00,… ## $ m200 <dbl> 22.94, 22.35, 23.09, 23.04, 23.05, 23.17, 24.47, 22.25,… ## $ m400 <dbl> 54.50, 51.08, 50.62, 52.00, 53.30, 52.80, 55.00, 50.06,… ## $ m800 <dbl> 2.15, 1.98, 1.99, 2.00, 2.16, 2.10, 2.18, 2.00, 2.05, 2… ## $ m1500 <dbl> 4.43, 4.13, 4.22, 4.14, 4.58, 4.49, 4.45, 4.06, 4.23, 4… ## $ m3000 <dbl> 9.79, 9.08, 9.34, 8.88, 9.81, 9.77, 9.51, 8.81, 9.37, 9… ## $ marathon <dbl> 178.52, 152.37, 159.37, 157.85, 169.98, 168.75, 191.02,… ## $ country <chr> "argentin", "australi", "austria", "belgium", "bermuda"… ``` .font_tiny[*Source*: Johnson and Wichern, Applied multivariate analysis] ]] --- class: split-30 layout: false .column[.pad10px[ ## Outline - High dimensions - PCA - .green[Example] - Data - Explore ]] .column[.top50px[  ]] --- class: split-30 layout: false .column[.pad10px[ ## Outline - High dimensions - PCA - .green[Example] - Data - Explore - Compute ]] .column[.top50px[ ```r track_pca <- prcomp(track[,1:7], center=TRUE, scale=TRUE) track_pca ``` ``` ## Standard deviations (1, .., p=7): ## [1] 2.41 0.81 0.55 0.35 0.23 0.20 0.15 ## ## Rotation (n x k) = (7 x 7): ## PC1 PC2 PC3 PC4 PC5 PC6 PC7 ## m100 0.37 0.49 -0.286 0.319 0.231 0.6198 0.052 ## m200 0.37 0.54 -0.230 -0.083 0.041 -0.7108 -0.109 ## m400 0.38 0.25 0.515 -0.347 -0.572 0.1909 0.208 ## m800 0.38 -0.16 0.585 -0.042 0.620 -0.0191 -0.315 ## m1500 0.39 -0.36 0.013 0.430 0.030 -0.2312 0.693 ## m3000 0.39 -0.35 -0.153 0.363 -0.463 0.0093 -0.598 ## marathon 0.37 -0.37 -0.484 -0.672 0.131 0.1423 0.070 ``` ]] --- class: split-30 layout: false .column[.pad10px[ ## Outline - High dimensions - PCA - .green[Example] - Data - Explore - Compute - Assess ]] .column[.top50px[ Summary of the principal components:

  	  PC1 	  PC2 	  PC3 	  PC4 	  PC5 	  PC6 	  PC7

  Variance 	  5.81 	  0.65 	  0.30 	  0.13 	  0.05 	  0.04 	  0.02

  Proportion 	  0.83 	  0.09 	  0.04 	  0.02 	  0.01 	  0.01 	  0.00

  Cum. prop 	  0.83 	  0.92 	  0.97 	  0.98 	  0.99 	  1.00 	  1.00

 Increase in variance explained large until `\(k=3\)` PCs, and then tapers off. A choice of .orange[3 PCs] would explain 97% of the total variance. ]] --- class: split-30 layout: false .column[.pad10px[ ## Outline - High dimensions - PCA - .green[Example] - Data - Explore - Compute - Assess ]] .column[.top50px[ Scree plot: Where is the elbow?  At `\(k=2\)`, thus the scree plot suggests 2 PCs would be sufficient to explain the variability. ]] --- class: split-30 layout: false .column[.pad10px[ ## Outline - High dimensions - PCA - .green[Example] - Data - Explore - Compute - Assess ]] .column[.top50px[ .boxshadow[.orange[.content[Visualise model using a biplot]]: Plot the principal component scores, and also the contribution of the original variables to the principal component. ]  ]] --- class: split-30 layout: true .column[.pad10px[ ## Outline - High dimensions - PCA - .green[Example] - Data - Explore - Compute - Assess - Interpret ]] .column[.top50px[ .row[.content[ .boxshadow[.orange[.content[Explain and interpret]]: using the coefficients of the principal components. ] - PC1 measures overall magnitude, the strength of the athletics program. High positive values indicate .orange[poor] programs with generally slow times across events. ]] .row[.content[ - PC2 measures the .orange[contrast] in the program between .orange[short and long distance] events. Some countries have relatively stronger long distance atheletes, while others have relatively stronger short distance athletes. ]] .row[.content[ - There are several .orange[outliers] visible in this plot, `wsamoa`, `cookis`, `dpkorea`. PCA, because it is computed using the variance in the data, can be affected by outliers. It may be better to remove these countries, and re-run the PCA. ]] ]] --- class: fade-row2-col2 fade-row3-col2 --- class:fade-row3-col2 count: false --- count: false --- class: split-30 layout: false .column[.pad10px[ ## Outline - High dimensions - PCA - .green[Example] - Data - Explore - Compute - Assess - Interpret - Accuracy ]] .column[.top50px[ Bootstrap can be used to assess whether the coefficients of a PC are significantly different from 0. The 95% bootstrap confidence intervals are:  All of the coefficients on PC1 are significantly different from 0, and positive, approximately equal, .orange[not significantly different from each other]. ]] --- layout: false # 👩💻 Made by a human with a computer ### Slides at [https://monba.dicook.org](https://monba.dicook.org). ### Code and data at [https://github.com/dicook/Business_Analytics](https://github.com/dicook/Business_Analytics).
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